Overview

Dataset statistics

Number of variables25
Number of observations1456
Missing cells548
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory452.2 KiB
Average record size in memory318.0 B

Variable types

Categorical2
Numeric23

Alerts

week_start_date has a high cardinality: 1049 distinct values High cardinality
year is highly correlated with reanalysis_max_air_temp_k and 1 other fieldsHigh correlation
ndvi_ne is highly correlated with ndvi_nw and 7 other fieldsHigh correlation
ndvi_nw is highly correlated with ndvi_ne and 7 other fieldsHigh correlation
ndvi_se is highly correlated with ndvi_ne and 2 other fieldsHigh correlation
ndvi_sw is highly correlated with ndvi_ne and 2 other fieldsHigh correlation
precipitation_amt_mm is highly correlated with reanalysis_dew_point_temp_k and 5 other fieldsHigh correlation
reanalysis_air_temp_k is highly correlated with reanalysis_avg_temp_k and 4 other fieldsHigh correlation
reanalysis_avg_temp_k is highly correlated with reanalysis_air_temp_k and 4 other fieldsHigh correlation
reanalysis_dew_point_temp_k is highly correlated with precipitation_amt_mm and 8 other fieldsHigh correlation
reanalysis_max_air_temp_k is highly correlated with year and 6 other fieldsHigh correlation
reanalysis_min_air_temp_k is highly correlated with ndvi_ne and 6 other fieldsHigh correlation
reanalysis_precip_amt_kg_per_m2 is highly correlated with precipitation_amt_mm and 5 other fieldsHigh correlation
reanalysis_relative_humidity_percent is highly correlated with precipitation_amt_mm and 4 other fieldsHigh correlation
reanalysis_sat_precip_amt_mm is highly correlated with precipitation_amt_mm and 5 other fieldsHigh correlation
reanalysis_specific_humidity_g_per_kg is highly correlated with precipitation_amt_mm and 7 other fieldsHigh correlation
reanalysis_tdtr_k is highly correlated with year and 7 other fieldsHigh correlation
station_avg_temp_c is highly correlated with reanalysis_air_temp_k and 6 other fieldsHigh correlation
station_diur_temp_rng_c is highly correlated with ndvi_ne and 5 other fieldsHigh correlation
station_max_temp_c is highly correlated with ndvi_ne and 5 other fieldsHigh correlation
station_min_temp_c is highly correlated with reanalysis_air_temp_k and 5 other fieldsHigh correlation
station_precip_mm is highly correlated with precipitation_amt_mm and 2 other fieldsHigh correlation
total_cases is highly correlated with reanalysis_min_air_temp_k and 1 other fieldsHigh correlation
ndvi_ne is highly correlated with ndvi_nw and 6 other fieldsHigh correlation
ndvi_nw is highly correlated with ndvi_ne and 6 other fieldsHigh correlation
ndvi_se is highly correlated with ndvi_ne and 2 other fieldsHigh correlation
ndvi_sw is highly correlated with ndvi_ne and 5 other fieldsHigh correlation
precipitation_amt_mm is highly correlated with reanalysis_sat_precip_amt_mmHigh correlation
reanalysis_air_temp_k is highly correlated with reanalysis_avg_temp_k and 5 other fieldsHigh correlation
reanalysis_avg_temp_k is highly correlated with reanalysis_air_temp_k and 5 other fieldsHigh correlation
reanalysis_dew_point_temp_k is highly correlated with reanalysis_air_temp_k and 5 other fieldsHigh correlation
reanalysis_max_air_temp_k is highly correlated with ndvi_ne and 6 other fieldsHigh correlation
reanalysis_min_air_temp_k is highly correlated with ndvi_ne and 6 other fieldsHigh correlation
reanalysis_precip_amt_kg_per_m2 is highly correlated with reanalysis_relative_humidity_percentHigh correlation
reanalysis_relative_humidity_percent is highly correlated with reanalysis_dew_point_temp_k and 2 other fieldsHigh correlation
reanalysis_sat_precip_amt_mm is highly correlated with precipitation_amt_mmHigh correlation
reanalysis_specific_humidity_g_per_kg is highly correlated with reanalysis_air_temp_k and 6 other fieldsHigh correlation
reanalysis_tdtr_k is highly correlated with ndvi_ne and 6 other fieldsHigh correlation
station_avg_temp_c is highly correlated with reanalysis_air_temp_k and 5 other fieldsHigh correlation
station_diur_temp_rng_c is highly correlated with ndvi_ne and 6 other fieldsHigh correlation
station_max_temp_c is highly correlated with reanalysis_avg_temp_k and 5 other fieldsHigh correlation
station_min_temp_c is highly correlated with reanalysis_air_temp_k and 5 other fieldsHigh correlation
ndvi_ne is highly correlated with ndvi_nwHigh correlation
ndvi_nw is highly correlated with ndvi_neHigh correlation
ndvi_se is highly correlated with ndvi_swHigh correlation
ndvi_sw is highly correlated with ndvi_seHigh correlation
precipitation_amt_mm is highly correlated with reanalysis_sat_precip_amt_mmHigh correlation
reanalysis_air_temp_k is highly correlated with reanalysis_avg_temp_k and 2 other fieldsHigh correlation
reanalysis_avg_temp_k is highly correlated with reanalysis_air_temp_k and 1 other fieldsHigh correlation
reanalysis_dew_point_temp_k is highly correlated with reanalysis_specific_humidity_g_per_kg and 1 other fieldsHigh correlation
reanalysis_max_air_temp_k is highly correlated with reanalysis_tdtr_k and 2 other fieldsHigh correlation
reanalysis_min_air_temp_k is highly correlated with reanalysis_air_temp_k and 1 other fieldsHigh correlation
reanalysis_precip_amt_kg_per_m2 is highly correlated with reanalysis_relative_humidity_percentHigh correlation
reanalysis_relative_humidity_percent is highly correlated with reanalysis_precip_amt_kg_per_m2High correlation
reanalysis_sat_precip_amt_mm is highly correlated with precipitation_amt_mmHigh correlation
reanalysis_specific_humidity_g_per_kg is highly correlated with reanalysis_dew_point_temp_k and 1 other fieldsHigh correlation
reanalysis_tdtr_k is highly correlated with reanalysis_max_air_temp_k and 1 other fieldsHigh correlation
station_avg_temp_c is highly correlated with reanalysis_avg_temp_k and 3 other fieldsHigh correlation
station_diur_temp_rng_c is highly correlated with reanalysis_max_air_temp_k and 2 other fieldsHigh correlation
station_max_temp_c is highly correlated with reanalysis_max_air_temp_k and 2 other fieldsHigh correlation
station_min_temp_c is highly correlated with reanalysis_air_temp_k and 1 other fieldsHigh correlation
city is highly correlated with year and 12 other fieldsHigh correlation
year is highly correlated with city and 7 other fieldsHigh correlation
weekofyear is highly correlated with reanalysis_air_temp_k and 7 other fieldsHigh correlation
ndvi_ne is highly correlated with city and 9 other fieldsHigh correlation
ndvi_nw is highly correlated with city and 8 other fieldsHigh correlation
ndvi_se is highly correlated with city and 3 other fieldsHigh correlation
ndvi_sw is highly correlated with city and 7 other fieldsHigh correlation
precipitation_amt_mm is highly correlated with reanalysis_sat_precip_amt_mmHigh correlation
reanalysis_air_temp_k is highly correlated with city and 10 other fieldsHigh correlation
reanalysis_avg_temp_k is highly correlated with weekofyear and 8 other fieldsHigh correlation
reanalysis_dew_point_temp_k is highly correlated with weekofyear and 8 other fieldsHigh correlation
reanalysis_max_air_temp_k is highly correlated with city and 16 other fieldsHigh correlation
reanalysis_min_air_temp_k is highly correlated with city and 16 other fieldsHigh correlation
reanalysis_relative_humidity_percent is highly correlated with city and 13 other fieldsHigh correlation
reanalysis_sat_precip_amt_mm is highly correlated with precipitation_amt_mmHigh correlation
reanalysis_specific_humidity_g_per_kg is highly correlated with weekofyear and 8 other fieldsHigh correlation
reanalysis_tdtr_k is highly correlated with city and 10 other fieldsHigh correlation
station_avg_temp_c is highly correlated with weekofyear and 9 other fieldsHigh correlation
station_diur_temp_rng_c is highly correlated with city and 9 other fieldsHigh correlation
station_max_temp_c is highly correlated with city and 5 other fieldsHigh correlation
station_min_temp_c is highly correlated with city and 9 other fieldsHigh correlation
total_cases is highly correlated with yearHigh correlation
ndvi_ne has 194 (13.3%) missing values Missing
ndvi_nw has 52 (3.6%) missing values Missing
ndvi_se has 22 (1.5%) missing values Missing
ndvi_sw has 22 (1.5%) missing values Missing
station_avg_temp_c has 43 (3.0%) missing values Missing
station_diur_temp_rng_c has 43 (3.0%) missing values Missing
station_max_temp_c has 20 (1.4%) missing values Missing
station_precip_mm has 22 (1.5%) missing values Missing
week_start_date is uniformly distributed Uniform
precipitation_amt_mm has 239 (16.4%) zeros Zeros
reanalysis_sat_precip_amt_mm has 239 (16.4%) zeros Zeros
station_precip_mm has 42 (2.9%) zeros Zeros
total_cases has 100 (6.9%) zeros Zeros

Reproduction

Analysis started2022-01-18 04:22:46.592958
Analysis finished2022-01-18 04:23:34.543787
Duration47.95 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

city
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
sj
936 
iq
520 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsj
2nd rowsj
3rd rowsj
4th rowsj
5th rowsj

Common Values

ValueCountFrequency (%)
sj936
64.3%
iq520
35.7%

Length

2022-01-17T22:23:34.579778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-17T22:23:34.624783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sj936
64.3%
iq520
35.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.031593
Minimum1990
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:34.667782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1991
Q11997
median2002
Q32005
95-th percentile2009
Maximum2010
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.408313996
Coefficient of variation (CV)0.002702762922
Kurtosis-0.8870678947
Mean2001.031593
Median Absolute Deviation (MAD)4
Skewness-0.4038904198
Sum2913502
Variance29.24986028
MonotonicityNot monotonic
2022-01-17T22:23:34.726847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2001104
 
7.1%
2007104
 
7.1%
2006104
 
7.1%
2005104
 
7.1%
2004104
 
7.1%
2003104
 
7.1%
2002104
 
7.1%
200078
 
5.4%
200869
 
4.7%
200952
 
3.6%
Other values (11)529
36.3%
ValueCountFrequency (%)
199035
2.4%
199152
3.6%
199252
3.6%
199352
3.6%
199452
3.6%
199552
3.6%
199652
3.6%
199752
3.6%
199852
3.6%
199952
3.6%
ValueCountFrequency (%)
201026
 
1.8%
200952
3.6%
200869
4.7%
2007104
7.1%
2006104
7.1%
2005104
7.1%
2004104
7.1%
2003104
7.1%
2002104
7.1%
2001104
7.1%

weekofyear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.50343407
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:34.805789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113.75
median26.5
Q339.25
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.01943718
Coefficient of variation (CV)0.5666977776
Kurtosis-1.19898123
Mean26.50343407
Median Absolute Deviation (MAD)13
Skewness0.001373119167
Sum38589
Variance225.5834934
MonotonicityNot monotonic
2022-01-17T22:23:34.880839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1828
 
1.9%
1928
 
1.9%
4628
 
1.9%
4728
 
1.9%
4828
 
1.9%
4928
 
1.9%
5028
 
1.9%
5128
 
1.9%
128
 
1.9%
228
 
1.9%
Other values (43)1176
80.8%
ValueCountFrequency (%)
128
1.9%
228
1.9%
328
1.9%
428
1.9%
528
1.9%
628
1.9%
728
1.9%
828
1.9%
928
1.9%
1028
1.9%
ValueCountFrequency (%)
535
 
0.3%
5223
1.6%
5128
1.9%
5028
1.9%
4928
1.9%
4828
1.9%
4728
1.9%
4628
1.9%
4528
1.9%
4428
1.9%

week_start_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1049
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size106.6 KiB
2005-06-18
 
2
2005-04-30
 
2
2005-02-19
 
2
2005-02-26
 
2
2005-03-05
 
2
Other values (1044)
1446 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)44.1%

Sample

1st row1990-04-30
2nd row1990-05-07
3rd row1990-05-14
4th row1990-05-21
5th row1990-05-28

Common Values

ValueCountFrequency (%)
2005-06-182
 
0.1%
2005-04-302
 
0.1%
2005-02-192
 
0.1%
2005-02-262
 
0.1%
2005-03-052
 
0.1%
2005-03-122
 
0.1%
2005-03-192
 
0.1%
2005-03-262
 
0.1%
2005-04-022
 
0.1%
2005-04-092
 
0.1%
Other values (1039)1436
98.6%

Length

2022-01-17T22:23:34.948822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2005-06-182
 
0.1%
2000-09-092
 
0.1%
2000-07-082
 
0.1%
2000-07-152
 
0.1%
2000-07-222
 
0.1%
2000-07-292
 
0.1%
2000-08-052
 
0.1%
2000-08-122
 
0.1%
2000-08-192
 
0.1%
2000-08-262
 
0.1%
Other values (1039)1436
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ndvi_ne
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1214
Distinct (%)96.2%
Missing194
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean0.1422935374
Minimum-0.40625
Maximum0.5083571
Zeros0
Zeros (%)0.0%
Negative171
Negative (%)11.7%
Memory size22.8 KiB
2022-01-17T22:23:35.014784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.40625
5-th percentile-0.07424
Q10.04495
median0.12881665
Q30.248483325
95-th percentile0.36356622
Maximum0.5083571
Range0.9146071
Interquartile range (IQR)0.203533325

Descriptive statistics

Standard deviation0.1405311531
Coefficient of variation (CV)0.9876144461
Kurtosis-0.1339580913
Mean0.1422935374
Median Absolute Deviation (MAD)0.0990119
Skewness-0.1108412278
Sum179.5744442
Variance0.019749005
MonotonicityNot monotonic
2022-01-17T22:23:35.089793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09693
 
0.2%
0.08023
 
0.2%
0.11943
 
0.2%
0.04553
 
0.2%
0.10722
 
0.1%
0.09792
 
0.1%
0.11212
 
0.1%
0.05682
 
0.1%
0.15352
 
0.1%
0.079652
 
0.1%
Other values (1204)1238
85.0%
(Missing)194
 
13.3%
ValueCountFrequency (%)
-0.406251
0.1%
-0.32141
0.1%
-0.30841
0.1%
-0.29021
0.1%
-0.2871
0.1%
-0.27611
0.1%
-0.26831
0.1%
-0.25171
0.1%
-0.24391
0.1%
-0.217951
0.1%
ValueCountFrequency (%)
0.50835711
0.1%
0.50102861
0.1%
0.49341
0.1%
0.48841
0.1%
0.48822861
0.1%
0.47501431
0.1%
0.46551
0.1%
0.45683331
0.1%
0.44708331
0.1%
0.44626671
0.1%

ndvi_nw
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1365
Distinct (%)97.2%
Missing52
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean0.1305525761
Minimum-0.4561
Maximum0.4544286
Zeros0
Zeros (%)0.0%
Negative173
Negative (%)11.9%
Memory size22.8 KiB
2022-01-17T22:23:35.166132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4561
5-th percentile-0.0542933305
Q10.0492166675
median0.1214286
Q30.2166
95-th percentile0.330700005
Maximum0.4544286
Range0.9105286
Interquartile range (IQR)0.1673833325

Descriptive statistics

Standard deviation0.1199990627
Coefficient of variation (CV)0.9191627332
Kurtosis0.05983516898
Mean0.1305525761
Median Absolute Deviation (MAD)0.0813857
Skewness-0.008168361413
Sum183.2958169
Variance0.01439977504
MonotonicityNot monotonic
2022-01-17T22:23:35.243756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08463
 
0.2%
0.078253
 
0.2%
0.02783
 
0.2%
0.11273
 
0.2%
0.25913332
 
0.1%
0.07052
 
0.1%
0.07022
 
0.1%
0.16112
 
0.1%
0.097252
 
0.1%
0.13392
 
0.1%
Other values (1355)1380
94.8%
(Missing)52
 
3.6%
ValueCountFrequency (%)
-0.45611
0.1%
-0.30961
0.1%
-0.25281
0.1%
-0.24981
0.1%
-0.21531
0.1%
-0.1786251
0.1%
-0.15381
0.1%
-0.14971
0.1%
-0.14121
0.1%
-0.1411
0.1%
ValueCountFrequency (%)
0.45442861
0.1%
0.4451
0.1%
0.43711
0.1%
0.43301431
0.1%
0.42877141
0.1%
0.42478331
0.1%
0.42291
0.1%
0.41131
0.1%
0.40831
0.1%
0.40507141
0.1%

ndvi_se
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1395
Distinct (%)97.3%
Missing22
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.203783189
Minimum-0.01553333
Maximum0.5383143
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size22.8 KiB
2022-01-17T22:23:35.322360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.01553333
5-th percentile0.094823095
Q10.155087475
median0.19605
Q30.248845825
95-th percentile0.341862005
Maximum0.5383143
Range0.55384763
Interquartile range (IQR)0.09375835

Descriptive statistics

Standard deviation0.07385973904
Coefficient of variation (CV)0.3624427481
Kurtosis0.5753176264
Mean0.203783189
Median Absolute Deviation (MAD)0.04572855
Skewness0.5733772074
Sum292.2250931
Variance0.005455261052
MonotonicityNot monotonic
2022-01-17T22:23:35.475979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.20221432
 
0.1%
0.19138572
 
0.1%
0.14634292
 
0.1%
0.15152
 
0.1%
0.21541432
 
0.1%
0.22337142
 
0.1%
0.23681432
 
0.1%
0.13431432
 
0.1%
0.22734292
 
0.1%
0.18831432
 
0.1%
Other values (1385)1414
97.1%
(Missing)22
 
1.5%
ValueCountFrequency (%)
-0.015533331
0.1%
0.0061833331
0.1%
0.028342861
0.1%
0.029881
0.1%
0.0361
0.1%
0.0391
0.1%
0.04461
0.1%
0.047814291
0.1%
0.051471431
0.1%
0.051481
0.1%
ValueCountFrequency (%)
0.53831431
0.1%
0.48428571
0.1%
0.47341
0.1%
0.455381
0.1%
0.44381
0.1%
0.44335711
0.1%
0.43774291
0.1%
0.43691431
0.1%
0.42768571
0.1%
0.42494291
0.1%

ndvi_sw
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1388
Distinct (%)96.8%
Missing22
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.2023054907
Minimum-0.06345714
Maximum0.5460167
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.1%
Memory size22.8 KiB
2022-01-17T22:23:35.553294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.06345714
5-th percentile0.088909
Q10.144208725
median0.18945
Q30.24698215
95-th percentile0.368839285
Maximum0.5460167
Range0.60947384
Interquartile range (IQR)0.102773425

Descriptive statistics

Standard deviation0.08390267779
Coefficient of variation (CV)0.4147325784
Kurtosis0.710497942
Mean0.2023054907
Median Absolute Deviation (MAD)0.05009285
Skewness0.754952134
Sum290.1060737
Variance0.00703965934
MonotonicityNot monotonic
2022-01-17T22:23:35.624301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.16887143
 
0.2%
0.18822863
 
0.2%
0.20994292
 
0.1%
0.17672862
 
0.1%
0.18305712
 
0.1%
0.18662862
 
0.1%
0.18795712
 
0.1%
0.19192862
 
0.1%
0.12978572
 
0.1%
0.14675712
 
0.1%
Other values (1378)1412
97.0%
(Missing)22
 
1.5%
ValueCountFrequency (%)
-0.063457141
0.1%
-0.022485711
0.1%
0.010251
0.1%
0.01161
0.1%
0.026028571
0.1%
0.028651
0.1%
0.0374751
0.1%
0.039433331
0.1%
0.040457141
0.1%
0.041885711
0.1%
ValueCountFrequency (%)
0.54601671
0.1%
0.54572861
0.1%
0.51482861
0.1%
0.49344291
0.1%
0.48987141
0.1%
0.47314291
0.1%
0.46972861
0.1%
0.46971
0.1%
0.46792861
0.1%
0.46572861
0.1%

precipitation_amt_mm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1157
Distinct (%)80.2%
Missing13
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean45.76038808
Minimum0
Maximum390.6
Zeros239
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:35.700351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.8
median38.34
Q370.235
95-th percentile121.631
Maximum390.6
Range390.6
Interquartile range (IQR)60.435

Descriptive statistics

Standard deviation43.71553699
Coefficient of variation (CV)0.9553139478
Kurtosis6.780028322
Mean45.76038808
Median Absolute Deviation (MAD)29.83
Skewness1.737448864
Sum66032.24
Variance1911.048174
MonotonicityNot monotonic
2022-01-17T22:23:35.776305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0239
 
16.4%
39.092
 
0.1%
59.42
 
0.1%
99.892
 
0.1%
74.012
 
0.1%
17.522
 
0.1%
65.822
 
0.1%
682
 
0.1%
74.082
 
0.1%
39.522
 
0.1%
Other values (1147)1186
81.5%
(Missing)13
 
0.9%
ValueCountFrequency (%)
0239
16.4%
0.491
 
0.1%
0.611
 
0.1%
0.631
 
0.1%
0.781
 
0.1%
1.021
 
0.1%
1.071
 
0.1%
1.131
 
0.1%
1.151
 
0.1%
1.171
 
0.1%
ValueCountFrequency (%)
390.61
0.1%
389.61
0.1%
287.551
0.1%
245.731
0.1%
243.551
0.1%
234.131
0.1%
224.91
0.1%
223.611
0.1%
214.761
0.1%
210.831
0.1%

reanalysis_air_temp_k
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1176
Distinct (%)81.3%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean298.7018524
Minimum294.6357143
Maximum302.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:35.857850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum294.6357143
5-th percentile296.5314286
Q1297.6589286
median298.6464286
Q3299.8335714
95-th percentile300.85
Maximum302.2
Range7.564285714
Interquartile range (IQR)2.174642856

Descriptive statistics

Standard deviation1.362419528
Coefficient of variation (CV)0.004561135181
Kurtosis-0.68168641
Mean298.7018524
Median Absolute Deviation (MAD)1.058571428
Skewness-0.08105671183
Sum431922.8786
Variance1.856186969
MonotonicityNot monotonic
2022-01-17T22:23:35.934390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.88571434
 
0.3%
297.04428573
 
0.2%
297.53714293
 
0.2%
300.543
 
0.2%
298.23857143
 
0.2%
299.64857143
 
0.2%
296.18428573
 
0.2%
298.29714293
 
0.2%
298.06142863
 
0.2%
298.04428573
 
0.2%
Other values (1166)1415
97.2%
(Missing)10
 
0.7%
ValueCountFrequency (%)
294.63571431
0.1%
294.84571431
0.1%
294.851
0.1%
294.92285711
0.1%
295.10428571
0.1%
295.12857141
0.1%
295.29714291
0.1%
295.35714291
0.1%
295.371
0.1%
295.38714291
0.1%
ValueCountFrequency (%)
302.21
0.1%
301.63714291
0.1%
301.49571431
0.1%
301.49285711
0.1%
301.47428571
0.1%
301.46571432
0.1%
301.41857141
0.1%
301.39714291
0.1%
301.34857141
0.1%
301.34428571
0.1%

reanalysis_avg_temp_k
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct600
Distinct (%)41.5%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean299.2255779
Minimum294.8928571
Maximum302.9285714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:36.013394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum294.8928571
5-th percentile297.1928571
Q1298.2571429
median299.2892857
Q3300.2071429
95-th percentile301.1196429
Maximum302.9285714
Range8.035714286
Interquartile range (IQR)1.95

Descriptive statistics

Standard deviation1.261715273
Coefficient of variation (CV)0.004216602343
Kurtosis-0.5299515604
Mean299.2255779
Median Absolute Deviation (MAD)0.9678571435
Skewness-0.1895440008
Sum432680.1857
Variance1.59192543
MonotonicityNot monotonic
2022-01-17T22:23:36.089394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.03571438
 
0.5%
300.28571438
 
0.5%
300.67857147
 
0.5%
299.84285717
 
0.5%
298.557
 
0.5%
300.20714297
 
0.5%
299.98571437
 
0.5%
299.94285717
 
0.5%
299.63571437
 
0.5%
300.63571436
 
0.4%
Other values (590)1375
94.4%
(Missing)10
 
0.7%
ValueCountFrequency (%)
294.89285711
0.1%
295.24285711
0.1%
295.42142861
0.1%
295.77857141
0.1%
295.99285711
0.1%
2961
0.1%
296.11
0.1%
296.11428571
0.1%
296.12857141
0.1%
296.17142862
0.1%
ValueCountFrequency (%)
302.92857141
0.1%
302.61428571
0.1%
302.21
0.1%
302.17857141
0.1%
302.16428571
0.1%
302.151
0.1%
3021
0.1%
301.93571431
0.1%
301.91428571
0.1%
301.87857141
0.1%

reanalysis_dew_point_temp_k
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1180
Distinct (%)81.6%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean295.2463565
Minimum289.6428571
Maximum298.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:36.172403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum289.6428571
5-th percentile292.4746429
Q1294.1189286
median295.6407143
Q3296.46
95-th percentile297.1239286
Maximum298.45
Range8.807142857
Interquartile range (IQR)2.341071428

Descriptive statistics

Standard deviation1.527809846
Coefficient of variation (CV)0.005174695006
Kurtosis-0.1100048135
Mean295.2463565
Median Absolute Deviation (MAD)1.027142858
Skewness-0.7214286743
Sum426926.2314
Variance2.334202926
MonotonicityNot monotonic
2022-01-17T22:23:36.243634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296.94
 
0.3%
296.64285714
 
0.3%
296.29571434
 
0.3%
295.87142864
 
0.3%
296.39714294
 
0.3%
296.07857143
 
0.2%
296.74142863
 
0.2%
293.273
 
0.2%
295.69571433
 
0.2%
296.45285713
 
0.2%
Other values (1170)1411
96.9%
(Missing)10
 
0.7%
ValueCountFrequency (%)
289.64285711
0.1%
289.82714291
0.1%
290.08857141
0.1%
290.37857141
0.1%
290.46714291
0.1%
290.55285711
0.1%
290.57285711
0.1%
290.63142861
0.1%
290.63428571
0.1%
290.63571431
0.1%
ValueCountFrequency (%)
298.451
0.1%
298.16142861
0.1%
298.14142861
0.1%
297.92857141
0.1%
297.91714291
0.1%
297.85285711
0.1%
297.79571431
0.1%
297.76428571
0.1%
297.74142861
0.1%
297.67857141
0.1%

reanalysis_max_air_temp_k
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct141
Distinct (%)9.8%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean303.4271093
Minimum297.8
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:36.316635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum297.8
5-th percentile299.5
Q1301
median302.4
Q3305.5
95-th percentile309.8
Maximum314
Range16.2
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.234600708
Coefficient of variation (CV)0.0106602232
Kurtosis-0.1860766353
Mean303.4271093
Median Absolute Deviation (MAD)1.8
Skewness0.8473253059
Sum438755.6
Variance10.46264174
MonotonicityNot monotonic
2022-01-17T22:23:36.390636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
302.335
 
2.4%
301.835
 
2.4%
302.432
 
2.2%
301.928
 
1.9%
30228
 
1.9%
302.627
 
1.9%
301.427
 
1.9%
302.927
 
1.9%
300.126
 
1.8%
302.226
 
1.8%
Other values (131)1155
79.3%
ValueCountFrequency (%)
297.81
 
0.1%
2981
 
0.1%
298.21
 
0.1%
298.41
 
0.1%
298.72
 
0.1%
298.83
 
0.2%
298.98
0.5%
2997
0.5%
299.18
0.5%
299.29
0.6%
ValueCountFrequency (%)
3141
0.1%
313.22
0.1%
312.91
0.1%
312.81
0.1%
312.71
0.1%
312.61
0.1%
312.41
0.1%
312.32
0.1%
312.22
0.1%
311.82
0.1%

reanalysis_min_air_temp_k
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct117
Distinct (%)8.1%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean295.7191563
Minimum286.9
Maximum299.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:36.545699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum286.9
5-th percentile291.225
Q1293.9
median296.2
Q3297.9
95-th percentile298.9
Maximum299.9
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.565364105
Coefficient of variation (CV)0.008675001434
Kurtosis-0.2095078885
Mean295.7191563
Median Absolute Deviation (MAD)1.9
Skewness-0.67351891
Sum427609.9
Variance6.58109299
MonotonicityNot monotonic
2022-01-17T22:23:36.623646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29837
 
2.5%
298.736
 
2.5%
298.433
 
2.3%
298.230
 
2.1%
297.530
 
2.1%
298.329
 
2.0%
294.328
 
1.9%
298.528
 
1.9%
298.828
 
1.9%
296.327
 
1.9%
Other values (107)1140
78.3%
ValueCountFrequency (%)
286.91
 
0.1%
287.21
 
0.1%
287.31
 
0.1%
287.41
 
0.1%
287.51
 
0.1%
287.81
 
0.1%
288.11
 
0.1%
288.22
0.1%
288.33
0.2%
288.42
0.1%
ValueCountFrequency (%)
299.91
 
0.1%
299.62
 
0.1%
299.55
 
0.3%
299.43
 
0.2%
299.39
 
0.6%
299.212
0.8%
299.112
0.8%
29921
1.4%
298.921
1.4%
298.828
1.9%

reanalysis_precip_amt_kg_per_m2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1039
Distinct (%)71.9%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean40.15181881
Minimum0
Maximum570.5
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:36.698646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.025
Q113.055
median27.245
Q352.2
95-th percentile122.05
Maximum570.5
Range570.5
Interquartile range (IQR)39.145

Descriptive statistics

Standard deviation43.4343989
Coefficient of variation (CV)1.081754207
Kurtosis22.23398352
Mean40.15181881
Median Absolute Deviation (MAD)16.945
Skewness3.384061331
Sum58059.53
Variance1886.547008
MonotonicityNot monotonic
2022-01-17T22:23:36.767703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.37
 
0.5%
116
 
0.4%
16.25
 
0.3%
21.55
 
0.3%
40.75
 
0.3%
16.45
 
0.3%
4.15
 
0.3%
10.35
 
0.3%
3.65
 
0.3%
16.75
 
0.3%
Other values (1029)1393
95.7%
(Missing)10
 
0.7%
ValueCountFrequency (%)
03
0.2%
0.11
 
0.1%
0.24
0.3%
0.32
0.1%
0.41
 
0.1%
0.52
0.1%
0.63
0.2%
0.72
0.1%
0.81
 
0.1%
0.91
 
0.1%
ValueCountFrequency (%)
570.51
0.1%
362.031
0.1%
304.521
0.1%
288.41
0.1%
279.61
0.1%
254.951
0.1%
253.31
0.1%
247.61
0.1%
238.21
0.1%
227.251
0.1%

reanalysis_relative_humidity_percent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1370
Distinct (%)94.7%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean82.1619591
Minimum57.78714286
Maximum98.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:36.844936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum57.78714286
5-th percentile72.96892857
Q177.17714286
median80.30142857
Q386.35785714
95-th percentile95.82
Maximum98.61
Range40.82285714
Interquartile range (IQR)9.180714286

Descriptive statistics

Standard deviation7.153897366
Coefficient of variation (CV)0.08707067656
Kurtosis-0.3919556275
Mean82.1619591
Median Absolute Deviation (MAD)3.832142857
Skewness0.5739193633
Sum118806.1929
Variance51.17824752
MonotonicityNot monotonic
2022-01-17T22:23:36.921139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.311428573
 
0.2%
95.078571432
 
0.1%
77.784285712
 
0.1%
79.502857142
 
0.1%
83.135714292
 
0.1%
79.517142862
 
0.1%
74.344285712
 
0.1%
75.435714292
 
0.1%
76.907142862
 
0.1%
96.522857142
 
0.1%
Other values (1360)1425
97.9%
(Missing)10
 
0.7%
ValueCountFrequency (%)
57.787142861
0.1%
60.641428571
0.1%
64.658571431
0.1%
64.688571431
0.1%
65.284285711
0.1%
66.042857141
0.1%
66.664285711
0.1%
66.735714291
0.1%
67.185714291
0.1%
67.212857141
0.1%
ValueCountFrequency (%)
98.611
0.1%
98.457142861
0.1%
98.341428571
0.1%
98.134285711
0.1%
98.132857141
0.1%
97.971428571
0.1%
97.941428571
0.1%
97.808571431
0.1%
97.775714291
0.1%
97.742857141
0.1%

reanalysis_sat_precip_amt_mm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1157
Distinct (%)80.2%
Missing13
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean45.76038808
Minimum0
Maximum390.6
Zeros239
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.001065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.8
median38.34
Q370.235
95-th percentile121.631
Maximum390.6
Range390.6
Interquartile range (IQR)60.435

Descriptive statistics

Standard deviation43.71553699
Coefficient of variation (CV)0.9553139478
Kurtosis6.780028322
Mean45.76038808
Median Absolute Deviation (MAD)29.83
Skewness1.737448864
Sum66032.24
Variance1911.048174
MonotonicityNot monotonic
2022-01-17T22:23:37.076073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0239
 
16.4%
39.092
 
0.1%
59.42
 
0.1%
99.892
 
0.1%
74.012
 
0.1%
17.522
 
0.1%
65.822
 
0.1%
682
 
0.1%
74.082
 
0.1%
39.522
 
0.1%
Other values (1147)1186
81.5%
(Missing)13
 
0.9%
ValueCountFrequency (%)
0239
16.4%
0.491
 
0.1%
0.611
 
0.1%
0.631
 
0.1%
0.781
 
0.1%
1.021
 
0.1%
1.071
 
0.1%
1.131
 
0.1%
1.151
 
0.1%
1.171
 
0.1%
ValueCountFrequency (%)
390.61
0.1%
389.61
0.1%
287.551
0.1%
245.731
0.1%
243.551
0.1%
234.131
0.1%
224.91
0.1%
223.611
0.1%
214.761
0.1%
210.831
0.1%

reanalysis_specific_humidity_g_per_kg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1171
Distinct (%)81.0%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean16.7464266
Minimum11.71571429
Maximum20.46142857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.159077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11.71571429
5-th percentile13.99678571
Q115.55714286
median17.08714286
Q317.97821429
95-th percentile18.72321429
Maximum20.46142857
Range8.745714286
Interquartile range (IQR)2.421071429

Descriptive statistics

Standard deviation1.542494255
Coefficient of variation (CV)0.09210885954
Kurtosis-0.4833444842
Mean16.7464266
Median Absolute Deviation (MAD)1.1
Skewness-0.5395752911
Sum24215.33286
Variance2.379288527
MonotonicityNot monotonic
2022-01-17T22:23:37.228554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.485714294
 
0.3%
18.091428574
 
0.3%
18.484285714
 
0.3%
17.704285714
 
0.3%
18.034285713
 
0.2%
17.638571433
 
0.2%
16.208571433
 
0.2%
18.207142863
 
0.2%
15.561428573
 
0.2%
17.295714293
 
0.2%
Other values (1161)1412
97.0%
(Missing)10
 
0.7%
ValueCountFrequency (%)
11.715714291
0.1%
11.964285711
0.1%
12.111428571
0.1%
12.331428571
0.1%
12.362857141
0.1%
12.365714291
0.1%
12.464285711
0.1%
12.481428571
0.1%
12.492857141
0.1%
12.582857141
0.1%
ValueCountFrequency (%)
20.461428571
0.1%
20.091428571
0.1%
20.078571431
0.1%
19.772857141
0.1%
19.754285711
0.1%
19.731428571
0.1%
19.617142861
0.1%
19.595714291
0.1%
19.448571431
0.1%
19.441
0.1%

reanalysis_tdtr_k
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct519
Distinct (%)35.9%
Missing10
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4.903754199
Minimum1.357142857
Maximum16.02857143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.299585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.357142857
5-th percentile1.885714286
Q12.328571429
median2.857142857
Q37.625
95-th percentile12.12142857
Maximum16.02857143
Range14.67142857
Interquartile range (IQR)5.296428571

Descriptive statistics

Standard deviation3.546445095
Coefficient of variation (CV)0.7232102082
Kurtosis-0.2002596562
Mean4.903754199
Median Absolute Deviation (MAD)0.7857142857
Skewness1.070845694
Sum7090.828571
Variance12.57727281
MonotonicityNot monotonic
2022-01-17T22:23:37.368555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.35714285721
 
1.4%
2.42857142917
 
1.2%
2.316
 
1.1%
2.47142857116
 
1.1%
2.24285714315
 
1.0%
2.57142857114
 
1.0%
2.07142857114
 
1.0%
2.32857142913
 
0.9%
2.15714285713
 
0.9%
2.54285714313
 
0.9%
Other values (509)1294
88.9%
ValueCountFrequency (%)
1.3571428571
 
0.1%
1.41
 
0.1%
1.4857142861
 
0.1%
1.51
 
0.1%
1.5714285711
 
0.1%
1.62
0.1%
1.6428571432
0.1%
1.6571428573
0.2%
1.6714285712
0.1%
1.73
0.2%
ValueCountFrequency (%)
16.028571431
0.1%
15.457142861
0.1%
14.942857141
0.1%
14.928571431
0.1%
14.91
0.1%
14.828571431
0.1%
14.642857141
0.1%
14.385714291
0.1%
14.285714291
0.1%
14.21
0.1%

station_avg_temp_c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct492
Distinct (%)34.8%
Missing43
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean27.18578337
Minimum21.4
Maximum30.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.448619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21.4
5-th percentile24.83714286
Q126.3
median27.41428571
Q328.15714286
95-th percentile28.94114286
Maximum30.8
Range9.4
Interquartile range (IQR)1.857142857

Descriptive statistics

Standard deviation1.292347463
Coefficient of variation (CV)0.04753762087
Kurtosis-0.1479307864
Mean27.18578337
Median Absolute Deviation (MAD)0.8642857143
Skewness-0.5678700748
Sum38413.5119
Variance1.670161965
MonotonicityNot monotonic
2022-01-17T22:23:37.598609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2817
 
1.2%
27.417
 
1.2%
27.214
 
1.0%
27.814
 
1.0%
28.314
 
1.0%
28.113
 
0.9%
26.613
 
0.9%
27.312
 
0.8%
27.612
 
0.8%
27.712
 
0.8%
Other values (482)1275
87.6%
(Missing)43
 
3.0%
ValueCountFrequency (%)
21.41
0.1%
22.842857141
0.1%
23.314285711
0.1%
23.41
0.1%
23.414285712
0.1%
23.614285711
0.1%
23.71
0.1%
23.714285711
0.1%
23.757142861
0.1%
23.914285711
0.1%
ValueCountFrequency (%)
30.81
0.1%
30.071428571
0.1%
30.033333331
0.1%
30.028571431
0.1%
301
0.1%
29.957142861
0.1%
29.91
0.1%
29.866666671
0.1%
29.657142861
0.1%
29.585714291
0.1%

station_diur_temp_rng_c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct470
Distinct (%)33.3%
Missing43
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean8.059328009
Minimum4.528571429
Maximum15.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.672617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.528571429
5-th percentile5.585714286
Q16.514285714
median7.3
Q39.566666667
95-th percentile12.2
Maximum15.8
Range11.27142857
Interquartile range (IQR)3.052380952

Descriptive statistics

Standard deviation2.128567654
Coefficient of variation (CV)0.2641122996
Kurtosis-0.2541487124
Mean8.059328009
Median Absolute Deviation (MAD)1.1
Skewness0.8452591506
Sum11387.83048
Variance4.530800257
MonotonicityNot monotonic
2022-01-17T22:23:37.748555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.18571428614
 
1.0%
6.913
 
0.9%
6.84285714313
 
0.9%
6.74285714313
 
0.9%
7.05714285712
 
0.8%
6.92857142912
 
0.8%
6.34285714312
 
0.8%
7.38571428612
 
0.8%
6.58571428612
 
0.8%
6.75714285712
 
0.8%
Other values (460)1288
88.5%
(Missing)43
 
3.0%
ValueCountFrequency (%)
4.5285714292
0.1%
4.5857142862
0.1%
4.6142857143
0.2%
4.6285714291
 
0.1%
4.6714285712
0.1%
4.6857142862
0.1%
4.71
 
0.1%
4.8285714292
0.1%
4.8428571431
 
0.1%
4.93
0.2%
ValueCountFrequency (%)
15.81
0.1%
14.91
0.1%
14.51
0.1%
14.133333332
0.1%
14.0751
0.1%
13.966666671
0.1%
13.5251
0.1%
13.52
0.1%
13.41
0.1%
13.361
0.1%

station_max_temp_c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)5.1%
Missing20
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean32.45243733
Minimum26.7
Maximum42.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.826867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum26.7
5-th percentile28.9
Q131.1
median32.8
Q333.9
95-th percentile35.4
Maximum42.2
Range15.5
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation1.959318211
Coefficient of variation (CV)0.06037507111
Kurtosis0.2215315492
Mean32.45243733
Median Absolute Deviation (MAD)1.1
Skewness-0.2620686601
Sum46601.7
Variance3.838927853
MonotonicityNot monotonic
2022-01-17T22:23:37.900868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.8146
 
10.0%
32.2126
 
8.7%
33.3112
 
7.7%
31.7112
 
7.7%
31.192
 
6.3%
33.973
 
5.0%
29.469
 
4.7%
30.663
 
4.3%
3055
 
3.8%
34.442
 
2.9%
Other values (63)546
37.5%
ValueCountFrequency (%)
26.72
 
0.1%
27.24
 
0.3%
27.817
 
1.2%
28.331
2.1%
28.941
2.8%
29.469
4.7%
3055
3.8%
30.11
 
0.1%
30.663
4.3%
30.91
 
0.1%
ValueCountFrequency (%)
42.21
 
0.1%
38.61
 
0.1%
37.71
 
0.1%
37.41
 
0.1%
37.21
 
0.1%
37.11
 
0.1%
374
0.3%
36.82
0.1%
36.72
0.1%
36.61
 
0.1%

station_min_temp_c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)5.1%
Missing14
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean22.10214979
Minimum14.7
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:37.983869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14.7
5-th percentile19.5
Q121.1
median22.2
Q323.3
95-th percentile24.4
Maximum25.6
Range10.9
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.574066225
Coefficient of variation (CV)0.0712177883
Kurtosis0.2239059557
Mean22.10214979
Median Absolute Deviation (MAD)1.1
Skewness-0.307107928
Sum31871.3
Variance2.47768448
MonotonicityNot monotonic
2022-01-17T22:23:38.062127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.9135
 
9.3%
22.8127
 
8.7%
23.3114
 
7.8%
21.7113
 
7.8%
24.4107
 
7.3%
22.2105
 
7.2%
21.199
 
6.8%
20.676
 
5.2%
2051
 
3.5%
2146
 
3.2%
Other values (63)469
32.2%
ValueCountFrequency (%)
14.71
0.1%
16.41
0.1%
16.81
0.1%
16.91
0.1%
171
0.1%
17.11
0.1%
17.21
0.1%
17.31
0.1%
17.41
0.1%
17.51
0.1%
ValueCountFrequency (%)
25.69
 
0.6%
2537
 
2.5%
24.4107
7.3%
24.21
 
0.1%
23.9135
9.3%
23.71
 
0.1%
23.63
 
0.2%
23.51
 
0.1%
23.42
 
0.1%
23.3114
7.8%

station_precip_mm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct663
Distinct (%)46.2%
Missing22
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean39.32635983
Minimum0
Maximum543.3
Zeros42
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:38.138144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q18.7
median23.85
Q353.9
95-th percentile127.845
Maximum543.3
Range543.3
Interquartile range (IQR)45.2

Descriptive statistics

Standard deviation47.4553143
Coefficient of variation (CV)1.206704981
Kurtosis15.37607645
Mean39.32635983
Median Absolute Deviation (MAD)18.05
Skewness2.984022989
Sum56394
Variance2252.006855
MonotonicityNot monotonic
2022-01-17T22:23:38.214130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042
 
2.9%
0.519
 
1.3%
3.311
 
0.8%
0.811
 
0.8%
1.310
 
0.7%
7.99
 
0.6%
2.39
 
0.6%
0.39
 
0.6%
4.68
 
0.5%
2.98
 
0.5%
Other values (653)1298
89.1%
(Missing)22
 
1.5%
ValueCountFrequency (%)
042
2.9%
0.39
 
0.6%
0.519
1.3%
0.65
 
0.3%
0.811
 
0.8%
17
 
0.5%
1.11
 
0.1%
1.310
 
0.7%
1.57
 
0.5%
1.62
 
0.1%
ValueCountFrequency (%)
543.31
0.1%
350.91
0.1%
305.91
0.1%
296.91
0.1%
293.11
0.1%
284.41
0.1%
282.71
0.1%
273.51
0.1%
270.91
0.1%
261.71
0.1%

total_cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct135
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.67513736
Minimum0
Maximum461
Zeros100
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2022-01-17T22:23:38.288137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median12
Q328
95-th percentile81.25
Maximum461
Range461
Interquartile range (IQR)23

Descriptive statistics

Standard deviation43.59600016
Coefficient of variation (CV)1.766798682
Kurtosis36.51253012
Mean24.67513736
Median Absolute Deviation (MAD)9
Skewness5.273849693
Sum35927
Variance1900.61123
MonotonicityNot monotonic
2022-01-17T22:23:38.362190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0100
 
6.9%
671
 
4.9%
570
 
4.8%
370
 
4.8%
269
 
4.7%
159
 
4.1%
758
 
4.0%
456
 
3.8%
844
 
3.0%
943
 
3.0%
Other values (125)816
56.0%
ValueCountFrequency (%)
0100
6.9%
159
4.1%
269
4.7%
370
4.8%
456
3.8%
570
4.8%
671
4.9%
758
4.0%
844
3.0%
943
3.0%
ValueCountFrequency (%)
4611
0.1%
4261
0.1%
4101
0.1%
3951
0.1%
3811
0.1%
3641
0.1%
3591
0.1%
3531
0.1%
3331
0.1%
3291
0.1%

Interactions

2022-01-17T22:23:31.535826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:51.912107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:53.881726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:55.598292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:57.386511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:59.118635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:00.844895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:02.663794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:04.528613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:06.408720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:08.269458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:10.012406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:11.861275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:13.736831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:15.525589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:17.512751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:19.347673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:21.153895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:22.934850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:24.588693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:26.328918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:28.118382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:29.840462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:31.608827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:52.016128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:53.954730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:55.675295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:57.470072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:22:59.200636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:00.925183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:02.747274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:04.613349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:06.490721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:08.346450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:10.094501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:11.947860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:13.817830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:15.689582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-17T22:23:17.597752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-01-17T22:23:31.469425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-17T22:23:38.516408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-17T22:23:38.715403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-17T22:23:38.913700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-17T22:23:39.114256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-17T22:23:33.252434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-17T22:23:33.772564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-17T22:23:34.098777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-17T22:23:34.423784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

cityyearweekofyearweek_start_datendvi_nendvi_nwndvi_sendvi_swprecipitation_amt_mmreanalysis_air_temp_kreanalysis_avg_temp_kreanalysis_dew_point_temp_kreanalysis_max_air_temp_kreanalysis_min_air_temp_kreanalysis_precip_amt_kg_per_m2reanalysis_relative_humidity_percentreanalysis_sat_precip_amt_mmreanalysis_specific_humidity_g_per_kgreanalysis_tdtr_kstation_avg_temp_cstation_diur_temp_rng_cstation_max_temp_cstation_min_temp_cstation_precip_mmtotal_cases
0sj1990181990-04-300.1226000.1037250.1984830.17761712.42297.572857297.742857292.414286299.8295.932.0073.36571412.4214.0128572.62857125.4428576.90000029.420.016.04
1sj1990191990-05-070.1699000.1421750.1623570.15548622.82298.211429298.442857293.951429300.9296.417.9477.36857122.8215.3728572.37142926.7142866.37142931.722.28.65
2sj1990201990-05-140.0322500.1729670.1572000.17084334.54298.781429298.878571295.434286300.5297.326.1082.05285734.5416.8485712.30000026.7142866.48571432.222.841.44
3sj1990211990-05-210.1286330.2450670.2275570.23588615.36298.987143299.228571295.310000301.4297.013.9080.33714315.3616.6728572.42857127.4714296.77142933.323.34.03
4sj1990221990-05-280.1962000.2622000.2512000.2473407.52299.518571299.664286295.821429301.9297.512.2080.4600007.5217.2100003.01428628.9428579.37142935.023.95.86
5sj1990231990-06-04NaN0.1748500.2543140.1817439.58299.630000299.764286295.851429302.4298.126.4979.8914299.5817.2128572.10000028.1142866.94285734.423.939.12
6sj1990241990-06-110.1129000.0928000.2050710.2102713.48299.207143299.221429295.865714301.3297.738.6082.0000003.4817.2342862.04285727.4142866.77142932.223.329.74
7sj1990251990-06-180.0725000.0725000.1514710.133029151.12299.591429299.528571296.531429300.6298.430.0083.375714151.1217.9771431.57142928.3714297.68571433.922.821.15
8sj1990261990-06-250.1024500.1461750.1255710.12360019.32299.578571299.557143296.378571302.1297.737.5182.76857119.3217.7900001.88571428.3285717.38571433.922.821.110
9sj1990271990-07-02NaN0.1215500.1606830.20256714.41300.154286300.278571296.651429302.3298.728.4081.28142914.4118.0714292.01428628.3285716.51428633.924.41.16

Last rows

cityyearweekofyearweek_start_datendvi_nendvi_nwndvi_sendvi_swprecipitation_amt_mmreanalysis_air_temp_kreanalysis_avg_temp_kreanalysis_dew_point_temp_kreanalysis_max_air_temp_kreanalysis_min_air_temp_kreanalysis_precip_amt_kg_per_m2reanalysis_relative_humidity_percentreanalysis_sat_precip_amt_mmreanalysis_specific_humidity_g_per_kgreanalysis_tdtr_kstation_avg_temp_cstation_diur_temp_rng_cstation_max_temp_cstation_min_temp_cstation_precip_mmtotal_cases
1446iq2010162010-04-230.2314860.2946860.3316570.24440086.70298.438571299.507143297.678571304.7294.781.4095.99571486.7019.4485717.75714327.8500009.60000033.522.551.18
1447iq2010172010-04-300.2397430.2592710.3077860.30794326.00299.048571300.028571296.468571308.4294.623.6087.65714326.0018.0685718.25714328.85000012.12500036.221.435.44
1448iq2010182010-05-070.2608140.2557860.2577710.34028673.97297.617143298.585714296.975714304.7294.685.4696.71285773.9718.6028575.71428627.6000009.60000033.221.48.12
1449iq2010192010-05-140.1686860.1585000.1330710.14560059.40297.278571297.935714296.738571306.0294.087.3097.44571459.4018.3914296.18571427.40000010.40000033.721.232.07
1450iq2010202010-05-210.2630710.2725000.2582710.2445001.15297.648571298.707143293.227143308.7290.18.8078.9985711.1514.90857111.24285725.6333339.20000034.020.02.56
1451iq2010212010-05-280.3427500.3189000.2563430.29251455.30299.334286300.771429296.825714309.7294.545.0088.76571455.3018.4857149.80000028.63333311.93333335.422.427.05
1452iq2010222010-06-040.1601570.1603710.1360430.22565786.47298.330000299.392857296.452857308.5291.9207.1091.60000086.4718.0700007.47142927.43333310.50000034.721.736.68
1453iq2010232010-06-110.2470570.1460570.2503570.23371458.94296.598571297.592857295.501429305.5292.450.6094.28000058.9417.0085717.50000024.4000006.90000032.219.27.41
1454iq2010242010-06-180.3339140.2457710.2788860.32548659.67296.345714297.521429295.324286306.1291.962.3394.66000059.6716.8157147.87142925.4333338.73333331.221.016.01
1455iq2010252010-06-250.2981860.2329710.2742140.31575763.22298.097143299.835714295.807143307.8292.336.9089.08285763.2217.35571411.01428627.4750009.90000033.722.220.44